Contact Centres in Canada – 2007; WRCarroll & THWagar Executive Overview
Bibliographic record
Abstract
extremely pleased with the response to the survey from contact centre managers from across the country. Over 320 managers from in-house and outsourced contact centre work establishments took time between May and September, 2007 to participate in this study which provides us with a rich source of data to examine this dynamic environment. Here are some highlights from our report. Contact Centres in Canada 2007: Over 66 percent of the survey respondents represented in-house contact centre work establishments with 34 percent representing outsourced and combination contact centres. Western Canada was almost 10 percent above the national average for in-house contact centres, whereas Atlantic Canada was almost 10 percent below the national average. Western and Central Canada both had a higher percentage of Canadian owned contact centres (approximately 75 % each), whereas Atlantic Canada shows a lower percentage of Canadian ownership (60 %) with a higher percentage of U.S. multi-national ownership (35 %). Atlantic Canada had much larger contact centres (43 % of the respondents in the region from contact centres with 300 employees plus) with a higher ratio of types of applications carried
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".